import base64 import io import json import os import uuid from datetime import datetime from pathlib import Path import pandas as pd import pytz import requests import streamlit as st from huggingface_hub import CommitScheduler, HfApi # File paths as constants PREDICTIONS_CSV = 'dis_predictions.csv' USERS_JSON = 'leaders/users.json' MATCHES_JSON = 'matches.json' OUTCOMES_JSON = 'match_outcomes.json' PLAYERS_JSON = 'players.json' image_path = 'ipl_image.png' PREDICTIONS_FOLDER = Path("ipl_predictions") PREDICTIONS_FOLDER.mkdir(parents=True, exist_ok=True) users_file = Path("leaders") / f"users.json" USERS_FOLDER = users_file.parent USERS_FOLDER.mkdir(parents=True, exist_ok=True) # Initialize CommitScheduler scheduler = CommitScheduler( repo_id="DIS_IPL_Dataset", repo_type="dataset", folder_path=PREDICTIONS_FOLDER, # Local folder where predictions are saved temporarily path_in_repo="ipl_predictions", # Path in dataset repo where predictions will be saved every=2, # Push every 240 minutes (4 hours) ) # Initialize CommitScheduler scheduler = CommitScheduler( repo_id="DIS_IPL_Dataset", repo_type="dataset", folder_path=USERS_FOLDER, # Local folder where users are saved temporarily path_in_repo="leaders", # Path in dataset repo where predictions will be saved every=2, # Push every 240 minutes (4 hours) ) # Initialize CSV and JSON files if they don't exist def initialize_files(): # Initialize predictions CSV try: pd.read_csv(PREDICTIONS_CSV) except FileNotFoundError: df = pd.DataFrame(columns=['user_name', 'match_id', 'predicted_winner', 'predicted_motm', 'bid_points']) df.to_csv(PREDICTIONS_CSV, index=False) def load_data(file_path): """ Load data from a JSON or CSV file. Args: file_path (str): The path to the file to load. Returns: pd.DataFrame or dict: The loaded data. """ try: if file_path.endswith('.json'): with open(file_path, 'r') as file: return json.load(file) elif file_path.endswith('.csv'): return pd.read_csv(file_path) except FileNotFoundError: if file_path.endswith('.json'): return {} elif file_path.endswith('.csv'): return pd.DataFrame() def get_base64_of_image(path): with open(path, "rb") as image_file: return base64.b64encode(image_file.read()).decode() # Get today's date in IST to load today's match def get_current_date_ist(): tz_IST = pytz.timezone('Asia/Kolkata') datetime_ist = datetime.now(tz_IST) return datetime_ist.strftime('%Y-%m-%d') # Function to get matches for today def get_today_matches(): today = get_current_date_ist() matches = load_data(MATCHES_JSON) today_matches = [match for match in matches if match['date'] == today] return today_matches # Function to check if prediction submission is allowed def is_submission_allowed(match_id): matches = load_data(MATCHES_JSON) # This loads matches correctly with IST times for match in matches: if match["match_id"] == match_id: # Parse the match start time in IST tz_IST = pytz.timezone('Asia/Kolkata') match_datetime_str = f'{match["date"]} {match["time"]}' # The match time string is like "2024-03-21 7:30 PM" match_datetime = datetime.strptime(match_datetime_str, "%Y-%m-%d %I:%M %p") match_datetime = tz_IST.localize(match_datetime) # Set the timezone to IST # Get the current time in IST current_datetime = datetime.now(tz_IST) if current_datetime > match_datetime: return False else: return True return False # If match_id not found, default to False def load_predictions(PREDICTIONS_CSV): try: return pd.read_csv(PREDICTIONS_CSV) except FileNotFoundError: return pd.DataFrame() # Submit prediction function def submit_prediction( user_name, match_id, predicted_winner, predicted_motm, bid_points, max_bid_points ): # Validation for user selection if user_name == "Select a user...": st.warning("Please select a valid user.") return # Check if prediction submission is allowed for the match if not is_submission_allowed(match_id): st.error("Prediction submission time has passed. Predictions can't be submitted after match start.") return if bid_points > max_bid_points: st.error(f"Your bid points exceed the 20% limit of your total points. Maximum allowed bid points: {max_bid_points}") return prediction_id = uuid.uuid4().hex prediction_date = datetime.now().strftime('%Y-%m-%d') prediction_data = { 'prediction_id': prediction_id, 'user_name': user_name, 'match_id': match_id, 'predicted_winner': predicted_winner, 'predicted_motm': predicted_motm, 'bid_points': bid_points, 'prediction_date': prediction_date # Include the prediction date } # Construct the filename to include match_id for easier retrieval prediction_file_name = f"prediction_{match_id}_{prediction_id}.json" prediction_file = PREDICTIONS_FOLDER / prediction_file_name with scheduler.lock: with prediction_file.open("a") as file: file.write(json.dumps(prediction_data)) file.write("\n") st.success("Prediction submitted successfully!") def get_user_total_points(user_name): users = load_data(USERS_JSON) return users.get(user_name, 0) # Define the new function def calculate_max_bid_points(user_name): total_points = get_user_total_points(user_name) max_bid_points = int(total_points * 0.20) # 20% of total points return max_bid_points def load_users(USERS_JSON): try: with open(USERS_JSON, 'r') as file: return json.load(file) except FileNotFoundError: return {} def user_selection_and_prediction(): users = list(load_data(USERS_JSON)) user_name = st.selectbox("Select User", ["Select a user..."] + users) max_bid_points = None if user_name != "Select a user...": max_bid_points = calculate_max_bid_points(user_name) st.write(f"Maximum bid points you can submit: {max_bid_points}") matches = get_today_matches() if matches: match_choice = st.selectbox("Select Today's Match", matches, format_func=lambda match: f"{match['teams'][0]} vs {match['teams'][1]}") match_id = match_choice['match_id'] teams = match_choice['teams'] predicted_winner = st.selectbox("Predicted Winner", teams) player_list = load_data(PLAYERS_JSON) predicted_motm = "" if predicted_winner in player_list: players = player_list[predicted_winner] predicted_motm = st.selectbox("Predicted Man of the Match", players) bid_points = st.number_input("Bid Points", min_value=1, value=100, format="%d") if st.button("Submit Prediction"): submit_prediction(user_name, match_id, predicted_winner, predicted_motm, bid_points, max_bid_points) else: st.write("No matches are scheduled for today.") def display_predictions(): if st.button("Show Predictions"): all_predictions = [] # Check if the directory exists if not os.path.exists(PREDICTIONS_FOLDER): st.write("No predictions directory found.") return # List all JSON files in the directory for filename in os.listdir(PREDICTIONS_FOLDER): if filename.endswith('.json'): file_path = os.path.join(PREDICTIONS_FOLDER, filename) # Read each JSON file and append its contents to the list with open(file_path, 'r') as file: prediction = json.load(file) all_predictions.append(prediction) # Convert the list of dictionaries to a DataFrame predictions_df = pd.DataFrame(all_predictions) if not predictions_df.empty: predictions_df['prediction_date'] = predictions_df.apply(lambda x: datetime.strptime(x['prediction_date'], '%Y-%m-%d'), axis=1) # Filter for today's predictions today_str = datetime.now().strftime('%Y-%m-%d') todays_predictions = predictions_df[predictions_df['prediction_date'] == today_str] # Remove the 'prediction_id' column if it exists if 'prediction_id' in todays_predictions.columns: todays_predictions = todays_predictions.drop(columns=['prediction_id', 'prediction_date']) st.dataframe(todays_predictions, hide_index=True) else: st.write("No predictions for today's matches yet.") def display_leaderboard(): if st.button("Show Leaderboard"): try: users = load_users(USERS_JSON) leaderboard = sorted(users.items(), key=lambda x: x[1], reverse=True) # Generate a list of dictionaries, each representing a row in the leaderboard leaderboard_dicts = [{"Rank": rank+1, "User": user[0], "Points": user[1]} for rank, user in enumerate(leaderboard)] # Convert the list of dictionaries to a DataFrame df_leaderboard = pd.DataFrame(leaderboard_dicts) st.dataframe(df_leaderboard, hide_index=True) except FileNotFoundError: st.write("Leaderboard data not available.") # Streamlit UI encoded_image = get_base64_of_image(image_path) custom_css = f""" """ # Apply custom CSS st.markdown(custom_css, unsafe_allow_html=True) # Use the custom class in a div with your title st.markdown('
DIS IPL Match Predictions
', unsafe_allow_html=True) st.write("🏆 Predict, Compete, and Win 🏏 - Where Every Guess Counts! 🏆") user_guide_content = """ ### 📘 User Guide #### Submitting Predictions - **Match Selection**: Choose the match you want to predict from today's available matches. - **Team and Player Prediction**: Select the team you predict will win and the "Man of the Match". - **Bid Points**: Enter the number of points you wish to bid on your prediction. Remember, the maximum you can bid is capped at 20% of your total points. #### Scoring System - **Winning Team Prediction**: Correct predictions earn you 1000 points, while incorrect predictions deduct 200 points. - **Man of the Match Prediction**: Correctly predicting the "Man of the Match" awards you 200 points. No penalty for incorrect guesses. - **Bonus Points**: An additional 200 points bonus is awarded for getting both the team and "Man of the Match" predictions right. #### Bid Point Constraints - You cannot bid more than 20% of your current total points. - Bid points will be doubled if your prediction is correct, and deducted if incorrect. #### Rules for Submission - Predictions must be submitted before the match starts. - Only one prediction per match is allowed. - Review your prediction carefully before submission, as it cannot be changed once submitted. """ # User Guide as an expander with st.expander("User Guide 📘"): st.markdown(user_guide_content) with st.expander("Submit Prediction 📝"): user_selection_and_prediction() with st.expander("Predictions 🔍"): display_predictions() with st.expander("Leaderboard 🏆"): display_leaderboard() ############################# Admin Panel ################################## ADMIN_PASSPHRASE = "admin123" from huggingface_hub import Repository # Define the local path to clone the repository local_repo_path = "DIS_IPL_Dataset" # Define your dataset repository name on Hugging Face Hub repo_id = "Jay-Rajput/DIS_IPL_Dataset" # Clone the repository (this only needs to be done once) repo = Repository(local_dir=local_repo_path, clone_from=repo_id) def fetch_latest_predictions(match_id): # Assuming predictions are stored in the "ipl_predictions" directory in your dataset repo predictions_path = Path("ipl_predictions") latest_predictions = [] for prediction_file in predictions_path.glob(f"prediction_{match_id}_*.json"): with open(prediction_file, 'r') as file: prediction_data = json.load(file) latest_predictions.append(prediction_data) return latest_predictions def save_match_outcomes(outcomes): with open(OUTCOMES_JSON, 'w') as file: json.dump(outcomes, file, indent=4) def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match): # Fetch latest predictions from the dataset repo predictions = fetch_latest_predictions(match_id) outcomes = load_data(OUTCOMES_JSON) # Load existing match outcomes users_file_path = repo.git_pull("leaders/users.json") with open(users_file_path, 'r') as file: users = json.load(file) # Directly update or add the match outcome outcome_exists = False for outcome in outcomes: if outcome['match_id'] == match_id: outcome.update({"winning_team": winning_team, "man_of_the_match": man_of_the_match}) outcome_exists = True break if not outcome_exists: outcomes.append({"match_id": match_id, "winning_team": winning_team, "man_of_the_match": man_of_the_match}) # Update user points based on prediction accuracy for prediction in predictions: user_name = prediction['user_name'] users[user_name] = users.get(user_name, 0) # Initialize user points if not present # Update points based on prediction accuracy if prediction['predicted_winner'] == winning_team: users[user_name] += 1000 users[user_name] += prediction['bid_points'] if prediction['predicted_motm'] == man_of_the_match: users[user_name] += 400 # Bonus for both correct predictions else: users[user_name] -= 200 + prediction['bid_points'] # Penalty for wrong team prediction save_match_outcomes(outcomes) # Save updated users data back to the dataset repo with open(users_file_path, 'w') as file: json.dump(users, file) # Commit changes to the dataset repo repo.git_add(users_file_path) repo.git_commit("Update leaderboard") repo.git_push() with st.sidebar: expander = st.expander("Admin Panel", expanded=False) admin_pass = expander.text_input("Enter admin passphrase:", type="password", key="admin_pass") if admin_pass == ADMIN_PASSPHRASE: expander.success("Authenticated") all_matches = load_data(MATCHES_JSON) match_outcomes = load_data(OUTCOMES_JSON) submitted_match_ids = [outcome["match_id"] for outcome in match_outcomes] # Filter matches to those that do not have outcomes submitted yet matches_without_outcomes = [match for match in all_matches if match["match_id"] not in submitted_match_ids] # If matches are available, let the admin select one if matches_without_outcomes: match_selection = expander.selectbox("Select Match", matches_without_outcomes, format_func=lambda match: f"{match['teams'][0]} vs {match['teams'][1]}", key="match_selection") selected_match_id = match_selection['match_id'] teams = match_selection['teams'] # Let admin select the winning team winning_team = expander.selectbox("Winning Team", teams, key="winning_team") # Fetch and display players for the selected winning team player_list = load_data(PLAYERS_JSON) if winning_team in player_list: players = player_list[winning_team] man_of_the_match = expander.selectbox("Man of the Match", players, key="man_of_the_match") else: players = [] man_of_the_match = expander.text_input("Man of the Match (Type if not listed)", key="man_of_the_match_fallback") if expander.button("Submit Match Outcome", key="submit_outcome"): update_leaderboard_and_outcomes(selected_match_id, winning_team, man_of_the_match) expander.success("Match outcome submitted and leaderboard updated!") else: expander.write("No matches are available for today.") else: if admin_pass: # Show error only if something was typed expander.error("Not authenticated")